In any experiment, variables are divided into two key types: independent and dependent. The independent variable is the factor that the researcher deliberately changes or manipulates to observe its effect on another variable. It’s essentially the “cause” that the experimenter controls to test its influence.
For example, imagine a study investigating how the amount of sugar in an espresso shot affects human nervousness or energy levels. Here, the amount of sugar is the independent variable because the researcher adjusts it directly. This manipulation is intended to reveal whether and how much the dependent variable (nervousness or energy) changes in response.
In short, the independent variable is what you change; the dependent variable is what you measure. Understanding this distinction is fundamental to designing experiments and interpreting causal relationships accurately.